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Introduction to Pandas


Pandas is an open source library that is used to analyze data in Python. It takes in data, like a CSV or SQL database, and creates an object with rows and columns called a data frame. Pandas is typically imported with the alias pd.

import pandas as pd

Pandas DataFrame creation

The fundamental Pandas object is called a DataFrame. It is a 2-dimensional size-mutable, potentially heterogeneous, tabular data structure.

A DataFrame can be created multiple ways. It can be created by passing in a dictionary or a list of lists to the pd.DataFrame() method, or by reading data from a CSV file.

# Ways of creating a Pandas DataFrame # Passing in a dictionary: data = {'name':['Anthony', 'Maria'], 'age':[30, 28]} df = pd.DataFrame(data) # Passing in a list of lists: data = [['Tom', 20], ['Jack', 30], ['Meera', 25]] df = pd.DataFrame(data, columns = ['Name', 'Age']) # Reading data from a csv file: df = pd.read_csv('students.csv')

Selecting Pandas DataFrame rows using logical operators

In pandas, specific rows can be selected if they satisfy certain conditions using Python’s logical operators. The result is a DataFrame that is a subset of the original DataFrame.

Multiple logical conditions can be combined with OR (using |) and AND (using &), and each condition must be enclosed in parentheses.

# Selecting rows where age is over 20 df[df.age > 20] # Selecting rows where name is not John df[ != "John"] # Selecting rows where age is less than 10 # OR greater than 70 df[(df.age < 10) | (df.age > 70)]

Pandas DataFrames adding columns

Pandas DataFrames allow for the addition of columns after the DataFrame has already been created, by using the format df['newColumn'] and setting it equal to the new column’s value.

# Specifying each value in the new column: df['newColumn'] = [1, 2, 3, 4] # Setting each row in the new column to the same value: df['newColumn'] = 1 # Creating a new column by doing a # calculation on an existing column: df['newColumn'] = df['oldColumn'] * 5

Pandas apply() function

The Pandas apply() function can be used to apply a function on every value in a column or row of a DataFrame, and transform that column or row to the resulting values.

By default, it will apply a function to all values of a column. To perform it on a row instead, you can specify the argument axis=1 in the apply() function call.

# This function doubles the input value def double(x): return 2*x # Apply this function to double every value in a specified column df.column1 = df.column1.apply(double) # Lambda functions can also be supplied to `apply()` df.column2 = df.column2.apply(lambda x : 3*x) # Applying to a row requires it to be called on the entire DataFrame df['newColumn'] = df.apply(lambda row: row['column1'] * 1.5 + row['column2'], axis=1 )

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